WS are simple, small and inexpensive devices and are not location dependent [GSB07]. These can be readily incorporated into mobile devices such as the popular i-Phone. In this paper we present a study on the effects of time on gait patterns in children and its relationships to gender and age.
The accelerometer sensor was placed on the left side of hip. A comparative analysis of gait patterns in children and adults both male and female for the purposes of recognition and identification is presented. The GP1 measures the acceleration in three per- pendicular directions which will be referred to as x, y and z. Figure 1 b is an example of the output obtained from the GP1 sensor and shows the signals obtained in the x, y and z directions. These signals provided the raw data for the subsequent analysis reported in later sections of this paper. It is converted into a unique pattern for each individual for comparison.
The GP1 can collect acceleration data up to 10g and has a sampling rate of Hz per axis. Acceleration data is filtered inside the GP1 by a 2 pole Butterworth low pass filter with a cut-off frequency of 45 Hz [Sen07]. The device has a USB interface for transferring data and a 1 Mbyte memory for storage purposes. An overview of the specification of the Sensr GP 1 is given in Table 1.
In this study, 46 children 31 boys and 15 girls with ages ranging between 5 to 16 years participated. For each child, the parents formally approved participation in the study by signing a standard University approval consent form prior to volunteering. The criteria set for the child to take part of this study were that they should have no previous history of injury to the lower extremities within the past year, and no known musculoskeletal or neurological disease. The Sensor Position, Right: Stop and wait for 5 seconds 7. Turn around and wait for 5 seconds 8.
Repeat procedure After walking twice the sensor was detached from the volunteer, connected to the computer and the data inside the GP1 device was downloaded and stored and the filed was named appropriately. The main experiment was carried out over a time period of 6 months. First experiment was performed in September and the second was performed in March There were 20 volunteers who participated in the long term experiment out of an initial group of In September , each subject did 2 sessions, whilst 16 sessions were performed in March This means that each subject participated in 18 sessions in total.
The feature extraction steps are based on the work of [DBH10]. First we apply linear time interpolation on the three axis data x,y,z re- 1 trieved from the sensor to obtain an observation every second since the time intervals between two observation points are not always equal. This noise is removed by using a weighted moving average filter WMA. The formula for WMA with a sliding window of size 5 is given in Equation 1.
The current value we are located at are given weight 3, the two closest neighbors weight 2 and the next two neighbors weight 1. Finally we calculate the resultant vector or the so-called magnitude vector by applying the following formula,. Therefore we need to get an estimation of how long one cycle is for each subject. This is done by extracting a small subset of the data and then comparing the subset with other subsets of similar lengths. Based on the distance scores between the subsets, the average cycle length is computed, as can be seen in Figure 3.
The cycle detection starts from a minimum point, Pstart , around the center of the walk. From this point, cycles are detected in both directions. The cycle end is defined to be the minimum in the interval Neighbour Search from the estimated end point. This is illustrated in Figure 4. This process is repeated from the new end point, until all the cycles are detected. The end point in the Neighbour Search is found by starting from point E. When the minimum point is found we store it into an array and we begin searching for the next minimum point by adding the length of one estimated cycle.
When forward searching is complete we repeat this phase by searching backwards so all steps in the data are identified. These points will therefore be used for the extraction of cycles, as illustrated in Figure 5. Before we create the feature vector template, we ensure that cycles that are very different from the others are skipped. The yellow baseline area indicates the subset with 70 samples that are extracted, the green area is the search area where the baseline is compared against a subset of the search area.
The 4 black subgraphs are the baseline at those points that has the lowest distance with the search area subsets, and the difference between them blue area indicate the cycle length [DBH10]. Cycle detection showing how each cycle i. This metric cross-compares two sets of cycles with a cyclic-rotation mechanism to find the best matching pair: This simply means that each cycle in the set C is compared to every cycle in the set C T.
The S comparative distances are calculated by the cyclic rotation metric CRM.
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The pair of cycles with the most minimum similarity score is considered the best matching pair. Thus, this best i. The reference cycle, i. CiS , which is compared against the input cycle, i. CjT , is stepwise cyclical rotated. After each rotation the new distance is calcu- lated using the Manhattan distance. This is repeated until the input template has done a full rotation, then the lowest distance value is kept: Furthermore the cyclic rotation is done to minimize the problem when local extremes among the cycles we create for each input are located at different locations. Three different tests have been performed and these are as follows: The first test analyzes the performance of gait and how it varies with the age of the children.
The second test analyzes the performance of gait and studies its variations over time, with a 6 months interval measurements. The third test analyzes and compares the performance of gait between boys and girls. As mentioned in a previous study by [Kyr02], it was suggested that the gait of children does not stabilize before they are 11 years old. The first group consisted of 17 children that are at most 10 years old. The second group consisted of the 11 children in our experiment that are only 10 years old, whilst the third group consisted of 18 children that were between 11 - 16 years old.
The split was done in this way because the size of the three groups is more or less equal. We do realize that the number of children in each of the three data sets is rather small, which influences the statistical significance of the results negatively.
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Nevertheless we want to present the results of our analysis on all three groups as an indication of the performance. The data used for the analysis is the collected gait data from March , i.
The resulting EER values are given in Table 2 for each of the three age groups. We also included the analysis results for the case where the group of 46 children was not split. From the results in Table 2 we see that with increasing age the EER value decreases, indicating an increase in the stability of the walking of children with increaing age. This seems to confirm the suggestion from [Kyr02]. In order to test this suggestion further we tested how the walking of children would change over time.
As mentioned in Section 2 do we have gait data samples from 20 children who participated in the experiment in both September and in March In September each of the 20 children provided only 2 gait data samples, but in March each of them provided 16 data samples. Of these 20 children, 18 were below 11 years old, one was exactly 11 years old and one who was 14 years old. We have determined the EER for each of these periods separately and we see in Table 3 that the resulting EER values are rather similar: In order to see if the gait has developed over time we also added a test where the template was created with the September data, while the test data came from the March data.
From Table 3 we see that the EER value increases significantly from approximately This indicates a major change in the way of walking of these children, confirming the suggestion from [Kyr02] once again. September March 6 Months Manh. Although the number of participants in the tests is rather low, we can still clearly see a change of performance over time.
Both these facts support the suggestion that the walking of children stabilizes around 11 years as Kyriazis suggested in [Kyr02]. A final test was performed to see if there are differences in gait recognition between boys and girls. The results can be found in Table 4. We know from Section 2 that the number of boys was more than twice the number of girls in the experiment conducted in March In order to make the results comparable we have used the gait data from all 15 girls and randomly selected 15 boys. The distance metric used was again the Manhattan with Rotation metric.
The slightly lower EER for girls The result in Table 4 for the boys is based on a random selection of 15 out of the available 31 boys. In order to make the result independent of the selected boys, this random selection has been performed times and the presented performance is the average over these results.
In this paper we have given evidence indicating the correctness of that suggestion. It has been shown that as the children get older their gait becomes more stable and that there is a large difference between the gait of a group of 20 young children measured six months apart; this indicates that the gait of children is still developing at these young ages. In addition, a comparison was carried out between the stability of gait from girls and boys and it was found that the female gait was slightly more stable as indicated by a lower EER.
Whilst the results presented in this study are interesting and in line with previous sug- gestions, a more comprehensive study with a higher number of participants is required to confirm the results described in this paper. In addition, research on the stability of gait from adults over a longer period of time is needed to compare against the results presented in this paper.
The writing of this article would not have been possible without their effort in the data collec- tion phase. On the Gait of Animals. Gait recognition in children under special circumstances. Exploratory factor analysis of gait recognition. In Au- tomatic Face Gesture Recognition, A giant leap for gait recognition. In Motion and Video Comput- ing, Gait recognition using acceleration from MEMS.
A wireless body area network of intelligent motion sensors for computer assisted physical reha- bilitation. Journal of NeuroEngineering and Rehabilitation, 2 1: Temporal gait analysis of children aged years. Journal of Or- thopaedics and Traumatology, 3: Iden- tifying users of portable devices from gait pattern with accelerometers. In Acoustics, Speech, and Signal Processing, Comparison of gait with and without shoes in children. Biomechanics of Lower Limb Prosthetics. Influence of carrying book bags on gait cycle and posture of youths.
In Ergonomics, volume 40, pages —, A sensor-based framework for detecting human gait cycles using acceleration signals. Distributed Diagnosis and Home Healthcare, 0: Automatic step detection in the accelerometer signal. Ubiquitous mobile devices like smartphones and tablets are often not se- cured against unauthorized access as the users tend to not use passwords because of convenience reasons.
Therefore, this study proposes an alternative user authentica- tion method for mobile devices based on gait biometrics. The gait characteristics are captured using the built-in accelerometer of a smartphone. Various features are ex- tracted from the measured accelerations and utilized to train a support vector machine SVM.
Among the extracted features are the Mel- and Bark-frequency cepstral coef- ficients MFCC, BFCC which are commonly used in speech and speaker recognition and have not been used for gait recognition previously. The proposed approach showed competitive recognition performance, yielding 5. For example in the United Kingdom about mobile phones were reported stolen every hour in , which lead to a call by the Minister of Crime Prevention to the mobile phone industry to better protect device owners against theft [CJ10].
A perpetrator having access to private and business emails, contacts and social networks can easily impersonate the victim. Al- though well-known technical security measures like PIN, password and strong encryption would protect the sensitive information, data protection techniques and knowledge based authentication with PIN and password are often not applied by the owner of the device even though they are available at essentially no additional cost.
There have been few phones with fingerprint scanners that never really entered mass mar- ket. The reasons are probably the rather high costs for the extra sensor that is not needed by the average end-user. Other biometric modalities, namely speaker, face and gait recogni- tion, do not have this problem as most modern phones are capable of realizing a biometric verification system using one or more of these modalities.
Compared to the mentioned modalities, gait recognition has one unique advantage: The driving motivation for gait recognition is that the device continuously authenticates the owner, when he is on the move and thus more rarely requires an explicit user authentica- tion. This study focuses on the last approach, wearable sensor WS based gait recognition. WS-based gait signals are captured by a sensor that is attached to the body, typically an accelerometer. Accelerome- ters used for gait recognition generally are tri-axial i.
When research in WS-based gait recognition started, dedicated sensors were used for data collection. But the interesting aspect of WS-based gait recognition is that accelerometers are in the meantime a standard component of mobile devices like smartphones and tablets e. The acceleration data was collected using a G1 smartphone with a customized application to access the accelerometer measurements and to output the data from the sensor to a file data points per second for each of the three directions x, y and z.
While recording the gait data the phone has been placed in a pouch attached to the belt of the subject see figure 1. In total, data of 48 healthy subjects was successfully recorded on two sessions at two different days with the subjects wearing their usual shoes and walking at normal pace. Age and gender distribution are given in table 1.
Age and gender distribution of data subjects. Position of phone during data collection. A gait cycle physically corresponds to two consecutive steps that the subject has taken, i.
Cycle-based features are computed by identifying gait cycles in time-series data representing a walking person. Then the feature extraction is conducted on identified cycles and the resulting features are used for biometric template creation and sample comparison. Currently this approach for represent- ing gait is the predominantly used method in gait recognition literature. As an alternative approach, a non-cycle-based gait representation, was used for this study.
Here, features are extracted from the times-series data from a selected time window without prior identifying the contained gait cycles. The collected gait samples are preprocessed such that the feature extraction algorithm works with consistent and portioned data. The first step is a linear interpolation to a fixed sampling rate as this is not given with the collected raw data. The average sampling rate of the raw data is about data points per second. The normalized acceleration signals are then separated into parts of several seconds using a sliding window approach with overlapping rectangular windows.
This means that the original signal of length l is splitted into segments of length t and distance d between con- secutive segments. The remaining part is dropped and not used any further. The segmen- tation is done for all normalized signals. The segments at this stage are still represented as time series.
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As the intention was to benefit from the well-performing classification capabilities of SVMs a transformation to a fixed length vector of discrete values has to be conducted. For each segment one feature vector is created. As a starting point statistical features were calculated for the acceleration signals: Further used features were the Mel-frequency cepstral coefficients MFCC and Bark- frequency cepstral coefficients BFCC , which belong to the most widely used spectral representations of audio signals for automatic speech recognition and speaker verification [GFK05].
The general workflow for creating MFCC is laid out in figure 2. A more elab- orate discussion can be found in [RJ93]. A SVM is a classifier which is inherently a solution for two class problems. The basic idea of the SVM is to construct a hyperplane as the decision plane, which separates the patterns of the two classes with the largest margin. In a first step the discrimination capabilities of single features and of combined features were investigated. The acceleration directions were also used separately to study their different contribution to the classification result. The acceleration signals collected on the first day were used for training and the signals collected on the second day were used for calculation of the recognition performance cross-day scenario.
First of all the data was interpolated to Hz. Further interpolation rates and segment lengths are tested later on. Last, the feature ex- traction was conducted for various feature types. To determine the biometric performance, cross-validation was conducted calculating the false match rate FMR and the false non- match rate FNMR. Five configurations were tested for each feature type using different data sources, namely the x-, y-, z-acceleration, the resulting acceleration, and all of them combined.
The results are given in table 2. All feature types have a resulting vector length of one. Exceptions are the binned distribu- tion, were five bins were used and thus a vector of length five is created. Of course, when the features are combined generated for each orientation and the magnitude as well the length is four times this number. It is apparent that the best performances are yielded when all sensor orientations and the magnitude as well is used. Now, various combinations of the features are tested.
The results are presented in table 3. The first four feature sets consist of combinations of the best performing single features. One can see that the results are basically the same. Therefore, further combinations of BFCC and several statistical features were tested set 8 to Evaluation results for interpolation rates of 50, and samples per second and a segment size of ms. For the seven best performing features sets set 5 to 11 , we evaluated the influence of the segment size. Table 4 gives the results for the new segment lengths 5, and 10,, and the previously used length of 7, ms.
One can see that the segment length of 10, performs best. No further segment lengths were evaluated as the length is limited by the duration of one walk. For the same feature sets, the interpolation frequency was varied from to 50 and samples per second. The results are given in table 5. The results of frequency and are nearly the same and slightly better than the ones of frequency The best result is a FNMR of Quorum voting scheme 5 Introduction of Voting Scheme With the yielded recognition performance it is very likely that a genuine is incorrectly rejected.
An imposter on the other side is rarely mis-classified and thus seldom falsely accepted. A post-processing approach that reduces the number of false rejects is to use multiple classifications for one recognition decision while incorporating a different confi- dence in the classification correctness. More specifically one uses V segments of a probe gait signal instead of only one segment for the recognition.
For each segment the classification is carried out as usually. Then, the V results are combined. An imaginable straightforward approach is majority voting, but it is not likely to perform well as there is such a large difference between the two error rates. Therefore a quorum voting for a genuine is implemented, which is inspired by a petition quorum. This quorum requires that at least GV positive classification results are obtained for an accept, otherwise the probe signal is rejected. The described concept is visualized in figure 3.
Note that Vg is the number of results that classify the respective segment as stemming from the genuine, in other words Vg is the number of votes for genuine. We conducted a series of experiments with the intention to find a balanced setting where both error rates are in the same range. Gait recognition performance with voting Various settings were evaluated to identify the optimal combination of votes V and gen- uine votes GV.
The reason for analysing the three different setups is to get an impression of the impact of time on the recognition results. The most relevant setup for a practical application is the cross-day performance, as in general the enrolment will take place on a different day than the authentication. We consider feature set 9 as the best setting, as it performed well in all evaluations and provides one of the best cross-day performances of 5.
One can see that the error rates greatly decrease when no separation between the days of collection is made. For the mixed-day setup we obtained a FNMR of 6. The difference to the same-day scenario indicates that having a larger training database with a greater intra-class variability results in better trained SVMs and hence in better recognition rates. The mixed-day results can be compared to some extend to our previous results using the same database.
Unfortunately the partition into training and testing data has not been the same in the three evaluations. In [DNBB10] a cycle extraction method was applied to the data. Reference cycles were extracted from the first walk collected on the first day, probe cycles were extracted from the remaining three walks. In this mixed-day scenario we obtained an equal error rate EER of All data from the first session and parts of the second session were used for training, the remaining parts of the second session were used for testing.
Nevertheless, a fair comparison of the stated methods is necessary. It offers the unique advantage of a truly unobtru- sive capturing of a biometric characteristic and is especially interesting for mobile devices that nowadays are sold with a suitable sensor already embedded. With future applications in mind the experiments in this study carefully distinguish be- tween the recognition performance on the same day and the recognition performance cross-days.
That this distinction is necessary becomes obvious by the good results with mixed and same-day tests compared to a rather weak performance of cross-day tests. Un- fortunately this has not been sufficiently addressed in most of the other gait recognition studies using wearable sensors.
Therefore it is questionable if the reported performances were capable of providing a good cross-day performance. In regard to the work carried out for this study the next task will be the porting of the developed program to the Android platform. One limitation of the current approach is that the training of the SVM requires feature vector instances of imposters which implies that a complete product would need to ship with a database with the feature instances. Although this is not impractical, from a privacy point of view it would be preferable to have a pre- trained SVM that is only further trained with the instances of the genuine user during enrolment.
Therefore incremental SVM learning approaches e. I addition, a practical solution should incorporate activity recognition to use only walking data for classification. In future we will analyze the performance of SVMs on a larger database, allowing a better training of the classifier. Another open question is whether the training of the SVMs can yield a better generalization in terms of a tolerance against inter-day gait variability when data from several days is used for training.
To answer this question a multi-day gait database is needed. Preferably this database will also contain different walking conditions shoes, underground etc. The authors thank the nu- merous volunteers that participated in the gait data collection. Iden- tifying people from gait pattern with accelerometers. Government calls for action on mobile phone crime. BBC News, February Software available at http: A Survey of Biometric Gait Recognition: Approaches, Security and Chal- lenges. Performance and security analysis of gait-based user authentication.
Comparative evaluation of various MFCC implementations on the speaker verification task. A practical guide to support vector classification. Cell Phone-Based Biometric Identi- fication. Theory, Applications and Systems, Fundamentals of speech recognition. A cumulant-based method for gait identification using accelerometer data with principal component analysis and support vector machine. Estimation of Dependences Based on Empirical Data: Springer-Verlag New York, Inc. This paper argues that biometrics have been re-framed in ways that de- couple them from the discourse of balancing liberty and security with profound implications for the exercise of democratically legitimated power at a time when the transformative impact of ICTs on governance and society is obscured by the pace of change and accelerating mobile forensic applications and policy intent.
It questions the framing of biometrics and the adequacy of law to combat the negative impact on society in the absence of EU governance and an independent EU certified quality standards agency for biometrics and associated training. Smart borders, registered traveller programmes, entry-exit logging, mobile e-transactions and rising user acceptance of dual-use biometrics show that within under ten years, biometrics have been re-framed in ways that de-couple them from the discourses of balancing liberty and security and those of safeguarding the public from potentially harmful technological innovations by respecting the precautionary principle to minimise risk.
This has profound implications for both the exercise of and trust in democratically legitimated power and what it means to be a person with a right to private life. The transformative power of new and soft biometrics on the scope and legitimacy of politico-legal authority and innovation governance has socio-ethical implications for society as well as for individuals.
These go beyond questions of how biometrics might be legitimately used, who owns them, who might be entitled to enrol and collect them, to how eIDs might help the state fulfil some of its tasks and how, under what conditions and subject to what quality standards and whose legal rules the private sector, or public-private partnerships PPPs might deliver services for government and for individuals, or might splice, commodify and commercialise data 1 f7p BEST project, and f7p ICTETHICS Thanks to anonymous reviewers for helpful comments.
Soft biometrics allow, and are part of, the commodification of the person, of his digital identity, of government and of society, and the de-privatisation of personal space and data. This is not synonymous with the end of secrecy, progressive openness or with legitimation. Should there be a limit to the legitimate deployment of biometrics? The first part of this paper explores some of the claims as to the advantages eID-enabled automated decisionmaking are expected to deliver in scalable, inter-operable applications.
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The second considers the tension between the technical holy grail of inter-operability and the reality of enhanced discriminatory social sorting and privacy invasiveness; and the third offers a reality check as to the implications for innovation in governance mediated by biometric apps. The socio-economic framing of the hard sell has been persuasive. For citizens, the prospect of a paper-less inter-operable means of undertaking all manner of transactions when, where and wherever they choose has proved equally attractive.
For both, the primary obstacles to deployment have been framed in terms of technical feasibility, sufficiently robust security architectures and cost; and the primary obstacles to wide-scale adoption in terms of ensuring compliance with legal regulations regarding data protection and data privacy, and trust in the systems and data handling. They imply that enrolment is a one-off process to establish a unique credential that is unvarying over time even though it is not.
User acceptance of biometrics was countered by framing automated identity management in terms of personal convenience gains to combat the association between biometrics and criminalisation of individuals as suspects, given the history of biometrics for forensic purposes notably detecting and verifying criminals, and profiling. This created exaggerated expectations of accuracy and reliability among the public when theft, fraud and data linkage to create fake identities showed that systems were far more vulnerable than the hype suggested. The same may be said of biometrics.
Digitisation assisted in the roll-out, linkage and exchange of such information, but the biometric per se was not simply a fingerprint, or associated digitised token. So the EU had an implicit purpose limitation focus that contextualised responsibility and accountability for deriving information, data and biometrics that did not accord with US practice. The subsequent more general acceptance of the transition from defining biometrics as a digitised representation of one or two physical characteristics of an individual fingerprint, iris scan, palm, voice or vein print, gait to a definition that includes behavioural characteristics incorporated in multimodal soft biometrics marks a shift in the technical capabilities and ambition of those developing and selling multi-modal, scalable, inter-operable applications, as well as in the impact on the nature and scope of public-private governance.
The shift marked therefore, an erosion of the pre-cautionary principle, and those of purpose limitation, data specification and data minimisation traditionally put forward where innovation may incur risk. The potentially risky impact of biometric applications on individuals and society seems initially to have been neglected by those grasping them as a panacea for managing modern government. Little attention has been paid to establishing quality standards and regimes against which risk and liabilities could be measured and appropriate assessments made as to their acceptability or otherwise: A shaming discourse developed around ethical issues as legislators focused on the scope and enforceability of existing requirements of compliance with data protection law and privacy rights, primarily by public authorities.
Instead, procedural aspects of data handling dominated while EU quality assurances for technical standard verification were neglected somewhat. The focus was on: Later, the issue of individual explicit consent-giving to data handling and sharing was considered as public awareness of spam from advertising trackers grew. Yet, the ability to exercise informed consent, a laudable principle derived from medical ethics, is context and ability dependant. Attempts to use technology to make its impact more neutral, and to preserve privacy by removing the need for explicit consent inspired privacy enhancing technology and privacy by design.
For example, the Turbine project developed algorithms to prevent identification without consent: The shaming discourse allowed biometrics to be presented as the problem and deflect attention from the real problem of how their use permitted linking a person to other genuine, created or inferred information 2.
Fuzziness over the concept of a biometric allowed its conflation with far wider concern over the impact of ICTs on individuals and society, their role in enabling dataveillance, and commercialisation of and subsequent ownership, processing and handling of, personal data.
Typically, questions were raised over: Brussels 20 June This shaming discourse shifted attention from technological quality and trust to potential or actual negative experiences when seeking to enrol or use a biometric or to challenge their deployment or adoption. For example, the speed gains of automated biometric gates such as the Privium iris recognition at Schiphol airport was seen as benefitting corporations, the rich, the able-bodied and those within the preferred age group in terms of the reliability of their biometrics.
Public awareness of the insufficiency of existing laws grew as biometrics were enrolled for general purposes library books, school registers, RFID implants as in Barcelona bars to pay for drinks , and as more casual breaches of data protection by public bodies rose. Capacity to prove who you claimed to be and the ability of government to protect the genuine identity, safety and security of an individual citizen were doubted.
Online fraud and crime seemed to enhance transactional risks to honest citizens who had been obliged to identify themselves by providing a fingerprint - an activity associated with criminal suspects. Many of the claims as to the beneficial and therapeutic effects of hard biometrics for all manner of transactions were eroded in the public mind, a situation that could have been worsened had there been more awareness of quality concerns and technical weaknesses.
The credibility of the shaming discourse arose from the uncritical acceptance of biometric identity management for diverse purposes, and from conflating understanding of what constituted a biometric with two things: The socio-ethical implications of adopting a behavioural definition of biometrics marked a paradigm shift in the relationship between the state and the citizen. The state could no longer uphold the classical claim to provide safety and security in return for loyalty. These are no longer solely under the legitimate control of governments and public authorities who can be held publicly accountable for error.
Private sector agencies, or private-public partnerships handle and process variable quality biometrics, and are responsible for accuracy and error. The locus of authority and responsibility is often invisible in the cloud or unknowable readily to the citizen as in the case of sub-contracting through third party out-sourcing. The claims made for using biometrics for multiple purposes — or even for the one click applications proposed by Google and others - pose serious questions over the legitimate use of power for: Multiple eIDs may contain different biometrics.
The need for biometrics continues to be asserted on realising an EU space of freedom, security and justice3. Distributive politics in the real world of public policymaking have eclipsed the need to establish mandatory EU quality standards on biometrics. The idea that biometrics are unethical rests not with the biometric per se but with the use to which a hard biometric iris scan or a soft biometric behaviour might be put. How it might be used in future, by whom and for what il legitimate purposes, is unknown.
EU law cannot be effective if there is confusion over what a quality, reliable biometric is. In biometricised society where is the legitimate locus of public authority, and how can it be made relevant when the transformative impact of technology, applications and redefinitions of biometrics require the re-thinking of what we mean by identity, trust and security, and by what it means to be human? Public and private space have been reconfigured and have entirely contingent meanings.
Biometric identifiers add a particular frisson to this. Obsolete legislation omitting references to quality standards, ICTs and digital applications allows ill-intended action to go unchecked, by default. The potential for harm is limitless.
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It allowed over 18s to tag but not withdraw or amend it. While civil liberty guardians assert this, corporate steps erase private profiles e. Online identity trade growth depends on no concealment of actual identity, and ending personal control over privacy and self-revelation. The counterpoint to the commercialisation and privatisation of the right to privacy and to be forgotten is the arbitrary exercise of power by invisible agencies whose location, legality and technical quality are unknown or hard to uncover.
Power is no longer visible, transparent or accountable in the public eye. Abuse of an unequal power relationship therefore endangers both privacy and security, eroding the rationale for using biometrics in the first place. Those who have supplied hard biometrics and those whose soft behavioural biometric might be inferred from surveillance are now highly visible. Those ab using them are not. The transformative capacity of the digital to embed tracking of the biometric token of a person should elicit deep concern over conflicts with ethics, and the impact on the EU of not having its own quality standards and certification.
However laudable the intention, there are still significant lags and mismatches between the law and technology: Purpose limitation and legitimate purpose are context contingent. But legitimate purpose needs to be coupled with quality and certification standards. The notion of legitimate purpose has been consistently expanded by security derogations and exceptions that raise serious socio-legal and ethical questions. Biometric, bio- medical and biopolitical data combinations are growing, and are applied for forensic purposes as well as commercial transactions, increasingly on mobile phones.
Such information can be mis- appropriated and ab used by private and public agencies for commercial gain, to invade privacy, to advance a given public policy goal or for as yet unknowable purposes. Should the public or public administrations place so much trust in non-standard biometrics when, apart from impact assessments, non-legislative measures, such as self- regulation, privacy seals, PETs and privacy by design principles, the only redress to growing distrust in automated data handling seems to be tighter regulation of data protection in case scalable inter-operability becomes a reality?
The disproportionate use of biometrics derived from or for mundane purposes - including online gaming data, avatar activity, advergaming and anything that can be captured as digitised behaviour - enables third party tracking, market sectorisation and commercialisation of private activity by adults and minors, intrudes on and shrinks private space, requires individuals to disclose more information biometric behaviour to access any service, including those ostensibly provided by government; shrinks the public space of government as a consequence of outsourcing and PPPs; privatises safety e.
The governance of innovative applications of technology has yet to be developed in ways designed to ensure quality and protect the individual who has a right to own and amend his personal data. How does this matter? Their reliability depends on age. Yet a biometric record may compromise the ability of a person to access their own authentic personal data.
Yet this test is not systematically and consistently applied to commercial data handling, collection, tracking, mining, re-selling, splicing and mashing — all of which might appeal to those aiming to exploit inter- operable scalable applications. Yet PNR is the public sector hook for making biometric IDs more or less mandatory, ubiquitous and acceptable, especially in Britain. However, in , the European Parliament deplored the failure of the contractor to implement state-of-the-art biometric systems.
High profile failures, such as the UK identity card scheme, telemedicine and health personal information leakages, negatively impact claims to the infallibility and reliability of biometrics against tampering, fraud and theft, as EU wide polls show. In cyber-space, avatar crime and human fraud are rife.
Privacy, security and safety have been eroded as much by commercially inspired ICT applications as by intent. Trust and accountability suffer. Public accountability comes to be redefined or emptied of meaning. As governments roll out counter cyber-crime strategies11, the question remains: Diverse liability and legal rules and regimes exacerbate discrimination. Commodifying eID management and privatising privacy protection through private, out-sourced commercial control over the handling of personal data and personal information means the public has no alternative to giving personal information to new private and allegedly trustworthy accountable providers.
Google exploited this in its data collection and mining initiatives permitting the comparison of financial services to present itself as responsible, and geared to non- disclosure of personal information. The corporate reality of arbitrary rule-making was thereby presented as a public good.
Recent Eurobarometer findings suggest that contrary to earlier findings, the public now trust public authorities more than companies to protect their personal data. Suspicion has risen in tandem with mobile applications12 over private company intent — whether mining of censoring data, and the accuracy of the claim that the biometric token is uniquely secure. Growing public awareness and cynicism over biometrics and their role in facilitating data linkage by invisible hands, makes the establishment of sustainable robust quality standards for EU biometric identity management urgent. Identity is not just a biometric key to access services.
How we define our identities and how we interact with each other as digital beings increasingly dependent on non- human and robotic interfaces in natural, virtual and personal spaces raises ethical questions. ICT designers may claim that they bake-in privacy; and legitimate authorities may claim they enforce rules when privacy is readily breached. Yet the lack of quality guarantees deprives the public of choice when compelled to provide hard biometric and other digitisable information, such as pseudo biometrics when tracked online. Do we want a free-for-all to allow the transformative capacity of ICTs and automated biometric identity management applications which are never error-free free rein in making unregulated, non-standardised, uncertified digi- tokens of imprecise quality the key to defining who we are for all manner of not always beneficent purposes?
Or should we rethink how their and our capacity for innovation be captured to design and facilitate ethical standards in EU governance and socio- commercial biometric applications to avert chaos? Bibliography [Aas06] Aas, K. Apple in July delayed emails and was believed to censor political content. Willan Publishing, ; pp. Inventory on politico-legal priorities in EU Prepared by Lodge J.
Constructing non human identities, presentation at ICT that makes the difference. Brussels, November , COM final, Brussels, Gesellschaft fur Informatik, e. Quantum Surveillance and shared secrets: Transparency, Justice and Territoriality: In Balzacq,T and Carrera, S. Are you Who you Say you are? Nijmegen, Wolf Legal Publishers, Social impact as a measure of fit between firm activities and stakeholder expectations. Whose Identity Is It Anyway? Biometric speaker verification deals with the recognition of voice and speech features to reliably identify a user and to offer him a comfortable alterna- tive to knowledge-based authentication methods like passwords.
As more and more personal data is saved on smartphones and other mobile devices, their secu- rity is in the focus of recent applications. Continuous Speaker Verification during smartphone phone calls offers a convenient way to improve the protection of these sensitive data. This paper describes an approach to realize a system for continuous speaker veri- fication during an ongoing phone call.
The aim of this research was to investigate the feasibility of such a system by creating a prototype. This prototype shows how it is possible to use existing technologies for speaker verification and speech rec- ognition to compute segments of a continuous audio signal in real-time. In line with experiments, a simulation study was made in which 14 subjects first trained the system with a freely spoken text and then verified themselves afterwards. Ad- ditional intruder tests against all other profiles where also simulated.
Introduction The protection of confidential data and the authentication of users to access these data are recently becoming more and more important. Especially with the growing amount of offers for different web portals and telephone-based services and the growing number of mobile devices, personal data tends to be saved in a distributed manner.
Mostly, identifi- cation numbers or passwords are used as authentication method. With growing compu- ting power and elaborate software to spy on passwords, they have to be longer and more complex to keep the data safe. For this reason biometric authentication processes are very promising. They use biome- tric features like fingerprints, the iris of the eye, the face, the voice or other biometric features or patterns of behavior for identification. It is presupposed that these features are unique for a person, even if they are not equally distinctive for everyone.
In common practice the verification is performed with a dedicated voice application, which explicitly asks the user for certain utterances that were optimally chosen for speech verification. An important application for such biometric verification systems is seen in mobile phones. Usually these devices are only protected by an identification number entered only at startup. Additional protection against intruders can be provided if voice verifica- tion is performed concurrent to phone calls.
If a speaker cannot be matched to the speech profile of the authorized user, the device could lock and the user would be asked to enter his personal identification number once more. In the investigation described in this paper the application scenario of a continuous real- time verification is evaluated.
The aim is to verify the user during any voice input, for example during a phone call. During the conversation, a continuous rating of the identity of the speaker is computed. The text independent verification of freely spoken language proved to be a particular challenge. In opposite to explicit verification dialogues the continuous verification system does not know the text of the spoken utterance in ad- vance. System Configuration The initial setup for the implementation of the prototype was based on the speech verifi- cation software VoxGuard created by atip GmbH.
The prototype uses VoxGuard for verification of single audio segments. Therefore the signal of a specific length with a sample rate of 8 kHz was sampled and quantized with a bit resolution. The features 1 that are important for speaker verification were extracted from the audio by means of spectral analysis. Every HMM represents a phoneme the smallest distinctive phone [2].
Altogether 41 different phoneme models were used. This allowed verification not just of known passphrases, but also of any possible utterance. To calculate Scores of the fea- tures with the help of HMMs, the Viterbi algorithm was used and a likelihood that a series of features build a certain HMM was determined. Before every verification the single phonemes were individually trained for every sub- ject. Here, several samples that contain an utterance with the needed phoneme were recorded. The extracted features were projected onto an initial model.
During this, the free parameters were calculated: This procedure was repeated with every training data set, so that the model adopted more and more trained data. This problem was solved by using a phoneme recognizer before the verification system. This recognizer uses speaker independent models to recognize any phoneme well. In order to recognize an arbitrary sequence of phonemes, a loop grammar was defined.
Inside this grammar, at every time, the occurrence of every phoneme of the inventory is allowed. In figure 1, the principle of this loop grammar is illustrated. Loop-Grammar for recognition of phoneme sequences. At every point in time t the scores for every phoneme model in the inventory is com- puted. With that, it is possible to build a two-dimensional Viterbi pattern which has exactly the width of the combined inventory of the phonemes and the length T. T is defined as the amount of blocks that were created during block-wise computing and the features that were extracted of them [3].
Then, the most probable path through this pat- tern is computed with the backtracking algorithm [4]. This path goes backwards along the states with the best score at any point in time. With this loop grammar and the speech recognizer it is possible to retrieve the most probable series of phonemes for an utterance. The recognition rate for single phonemes is quite low though.
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